code: def test_autocast_kwargs(self):
      kwargs = AutocastKwargs(enabled=False)
      AcceleratorState._reset_state()
      accelerator = Accelerator(mixed_precision="fp16")
  
      a_float32 = torch.rand((8, 8), device=accelerator.device)
      b_float32 = torch.rand((8, 8), device=accelerator.device)
      c_float32 = torch.rand((8, 8), device=accelerator.device)
      d_float32 = torch.rand((8, 8), device=accelerator.device)
  
      with accelerator.autocast():
          e_float16 = torch.mm(a_float32, b_float32)
          assert e_float16.dtype == torch.float16
  
          with accelerator.autocast(autocast_handler=kwargs):
              # Convert e_float16 to float32
              f_float32 = torch.mm(c_float32, e_float16.float())
              assert f_float32.dtype == torch.float32
  
          g_float16 = torch.mm(d_float32, f_float32)
          # We should be back in fp16
          assert g_float16.dtype == torch.float16
  
file_path: accelerate/tests/test_kwargs_handlers.py
imports: import inspect
  import os
  import unittest
  from dataclasses import dataclass
  import torch
  from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
  from accelerate.state import AcceleratorState
  from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
  from accelerate.utils import AutocastKwargs, KwargsHandler, TorchDynamoPlugin, clear_environment
label_desc: Use image node_id 4 for calling the KwargsHandlerTester obj's underlying member method code with example usage: obj.test_autocast_kwargs() without return types